code: def drq_image_augmentation(key, obs, img_pad=4):
      """Padding and cropping for DrQ."""
      flat_obs = obs.reshape(-1, *obs.shape[-3:])
      paddings = [
          (0, 0),
          (img_pad, img_pad),
          (img_pad, img_pad),
          (0, 0),
      ]
      cropped_shape = flat_obs.shape
      # The reference uses ReplicationPad2d in pytorch, but it is not available
      # in Jax. Use 'edge' instead.
      flat_obs = jnp.pad(flat_obs, paddings, "edge")
      key1, key2 = jax.random.split(key, num=2)
      cropped_obs = _per_image_random_crop(
          key2, flat_obs, cropped_shape
      )
      # cropped_obs = _random_crop(key2, flat_obs, cropped_shape)
      aug_obs = _intensity_aug(key1, cropped_obs)
      return aug_obs.reshape(*obs.shape)
  
imports: import copy
  import functools
  from absl import logging
  from dopamine.jax import networks
  from dopamine.jax.agents.full_rainbow import full_rainbow_agent
  import gin
  import jax
  import jax.numpy
  import numpy
  import tensorflow
label_desc: Use image node_id 5 for calling a global function with example usage: drq_image_augmentation(key, obs, img_pad) and returns: aug_obs